





"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import MobileNetVaImageProcessor

class 						lowercase   (   unittest.TestCase   ):





      def __init__(			self ,		lowercase ,		lowercase=7 ,		lowercase=3 ,		lowercase=18 ,		lowercase=30 ,		lowercase=400 ,		lowercase=True ,		lowercase=None ,		lowercase=True ,		lowercase=None ,		)						->	List[str]:
            lowerCAmelCase    		=			size if size is not None else {"""shortest_edge""": 20}
            lowerCAmelCase    		=			crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
            lowerCAmelCase    		=			parent
            lowerCAmelCase    		=			batch_size
            lowerCAmelCase    		=			num_channels
            lowerCAmelCase    		=			image_size
            lowerCAmelCase    		=			min_resolution
            lowerCAmelCase    		=			max_resolution
            lowerCAmelCase    		=			do_resize
            lowerCAmelCase    		=			size
            lowerCAmelCase    		=			do_center_crop
            lowerCAmelCase    		=			crop_size





      def        _snake_case     (			self					)						->	str:
            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_center_crop": self.do_center_crop,
                "crop_size": self.crop_size,
            }







@require_torch
@require_vision
class 						lowercase   (   _UpperCAmelCase	,    unittest.TestCase   ):
      _SCREAMING_SNAKE_CASE							      = MobileNetVaImageProcessor if is_vision_available() else None





      def        _snake_case     (			self					)						->	int:
            lowerCAmelCase    		=			MobileNetVaImageProcessingTester(self					)





      @property
      def        _snake_case     (			self					)						->	List[Any]:
            return self.image_processor_tester.prepare_image_processor_dict()





      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			self.image_processing_class(**self.image_processor_dict					)
            self.assertTrue(hasattr(lowercase ,		"""do_resize"""					)					)
            self.assertTrue(hasattr(lowercase ,		"""size"""					)					)
            self.assertTrue(hasattr(lowercase ,		"""do_center_crop"""					)					)
            self.assertTrue(hasattr(lowercase ,		"""crop_size"""					)					)





      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase    		=			self.image_processing_class.from_dict(self.image_processor_dict					)
            self.assertEqual(image_processor.size ,		{"""shortest_edge""": 20}					)
            self.assertEqual(image_processor.crop_size ,		{"""height""": 18, """width""": 18}					)

            lowerCAmelCase    		=			self.image_processing_class.from_dict(self.image_processor_dict ,		size=42 ,		crop_size=84					)
            self.assertEqual(image_processor.size ,		{"""shortest_edge""": 42}					)
            self.assertEqual(image_processor.crop_size ,		{"""height""": 84, """width""": 84}					)





      def        _snake_case     (			self					)						->	Tuple:
            pass





      def        _snake_case     (			self					)						->	Dict:
            # Initialize image_processing
            lowerCAmelCase    		=			self.image_processing_class(**self.image_processor_dict					)
            # create random PIL images
            lowerCAmelCase    		=			prepare_image_inputs(self.image_processor_tester ,		equal_resolution=lowercase					)
            for image in image_inputs:
                  self.assertIsInstance(lowercase ,		Image.Image					)

            # Test not batched input
            lowerCAmelCase    		=			image_processing(image_inputs[0] ,		return_tensors="""pt"""					).pixel_values
            self.assertEqual(
                encoded_images.shape ,		(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                ) ,		)

            # Test batched
            lowerCAmelCase    		=			image_processing(lowercase ,		return_tensors="""pt"""					).pixel_values
            self.assertEqual(
                encoded_images.shape ,		(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                ) ,		)





      def        _snake_case     (			self					)						->	List[Any]:
            # Initialize image_processing
            lowerCAmelCase    		=			self.image_processing_class(**self.image_processor_dict					)
            # create random numpy tensors
            lowerCAmelCase    		=			prepare_image_inputs(self.image_processor_tester ,		equal_resolution=lowercase ,		numpify=lowercase					)
            for image in image_inputs:
                  self.assertIsInstance(lowercase ,		np.ndarray					)

            # Test not batched input
            lowerCAmelCase    		=			image_processing(image_inputs[0] ,		return_tensors="""pt"""					).pixel_values
            self.assertEqual(
                encoded_images.shape ,		(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                ) ,		)

            # Test batched
            lowerCAmelCase    		=			image_processing(lowercase ,		return_tensors="""pt"""					).pixel_values
            self.assertEqual(
                encoded_images.shape ,		(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                ) ,		)





      def        _snake_case     (			self					)						->	List[Any]:
            # Initialize image_processing
            lowerCAmelCase    		=			self.image_processing_class(**self.image_processor_dict					)
            # create random PyTorch tensors
            lowerCAmelCase    		=			prepare_image_inputs(self.image_processor_tester ,		equal_resolution=lowercase ,		torchify=lowercase					)
            for image in image_inputs:
                  self.assertIsInstance(lowercase ,		torch.Tensor					)

            # Test not batched input
            lowerCAmelCase    		=			image_processing(image_inputs[0] ,		return_tensors="""pt"""					).pixel_values
            self.assertEqual(
                encoded_images.shape ,		(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                ) ,		)

            # Test batched
            lowerCAmelCase    		=			image_processing(lowercase ,		return_tensors="""pt"""					).pixel_values
            self.assertEqual(
                encoded_images.shape ,		(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                ) ,		)













"""simple docstring"""





import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 						lowercase   (   unittest.TestCase   ):





      @property
      def        _snake_case     (			self					)						->	List[str]:
            torch.manual_seed(0					)
            lowerCAmelCase    		=			UNetaDModel(
                block_out_channels=(32, 64) ,		layers_per_block=2 ,		sample_size=32 ,		in_channels=3 ,		out_channels=3 ,		down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") ,		up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") ,		)
            return model





      def        _snake_case     (			self					)						->	List[Any]:
            lowerCAmelCase    		=			self.dummy_uncond_unet
            lowerCAmelCase    		=			ScoreSdeVeScheduler()

            lowerCAmelCase    		=			ScoreSdeVePipeline(unet=lowercase ,		scheduler=lowercase					)
            sde_ve.to(lowercase					)
            sde_ve.set_progress_bar_config(disable=lowercase					)

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=2 ,		output_type="""numpy""" ,		generator=lowercase					).images

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=2 ,		output_type="""numpy""" ,		generator=lowercase ,		return_dict=lowercase					)[
                0
            ]

            lowerCAmelCase    		=			image[0, -3:, -3:, -1]
            lowerCAmelCase    		=			image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            lowerCAmelCase    		=			np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
            assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2







@slow
@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase    		=			"""google/ncsnpp-church-256"""
            lowerCAmelCase    		=			UNetaDModel.from_pretrained(lowercase					)

            lowerCAmelCase    		=			ScoreSdeVeScheduler.from_pretrained(lowercase					)

            lowerCAmelCase    		=			ScoreSdeVePipeline(unet=lowercase ,		scheduler=lowercase					)
            sde_ve.to(lowercase					)
            sde_ve.set_progress_bar_config(disable=lowercase					)

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=10 ,		output_type="""numpy""" ,		generator=lowercase					).images

            lowerCAmelCase    		=			image[0, -3:, -3:, -1]

            assert image.shape == (1, 256, 256, 3)

            lowerCAmelCase    		=			np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2






